All Videos Tagged Silos” (Data Science Central) - Data Science Central 2019-12-08T22:38:02Z https://www.datasciencecentral.com/video/video/listTagged?tag=Silos%E2%80%9D&rss=yes&xn_auth=no DSC Webinar Series: Data Mastering at Scale tag:www.datasciencecentral.com,2019-10-30:6448529:Video:903806 2019-10-30T00:48:10.189Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-mastering-at-scale"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3686726325?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Master data management (MDM) software turned 15 years old this year.<br></br> <br></br> Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-mastering-at-scale"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3686726325?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Master data management (MDM) software turned 15 years old this year.<br /> <br /> Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT systems.<br /> <br /> MDM is valuable, but it’s also slow, labor-intensive, and costly. As the scale of MDM projects increases to millions of entities and hundreds or thousands of data sources, the traditional methods often fail.<br /> <br /> Mike Stonebraker will share his view on how MDM technology and MDM organizations must change to fulfill the promise of MDM at scale. In this latest Data Science Central webinar, we will review:<br /> <br /> Why large enterprises need data management solutions that solve data mastering challenges at scale<br /> Why traditional, rule-based, data mastering options are struggling to keep up<br /> How Machine Learning can be used to address large-scale data mastering challenges<br /> <br /> Speaker:<br /> Mike Stonebraker, CTO &amp; Co-Founder - Tamr, Inc.<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: 4 Ways to Tackle Common Data Prep Issues tag:www.datasciencecentral.com,2018-09-25:6448529:Video:763220 2018-09-25T21:15:12.271Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-4-ways-to-tackle-common-data-prep-issues"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/2781532499?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Anyone who's ever analyzed data knows the pain of digging in only to find that it is poorly structured, full of inaccuracies, or just plain incomplete. But "dirty data" isn't just a pain point for analysts; it can have a major financial and cultural impact on an organization.<br></br> <br></br> In this latest Data Science Central webinar, you will… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-4-ways-to-tackle-common-data-prep-issues"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781532499?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Anyone who's ever analyzed data knows the pain of digging in only to find that it is poorly structured, full of inaccuracies, or just plain incomplete. But "dirty data" isn't just a pain point for analysts; it can have a major financial and cultural impact on an organization.<br /> <br /> In this latest Data Science Central webinar, you will learn four actionable ways to overcome common data preparation issues, including how to establish a company standard for "clean data" and how to democratize data prep across your organization.<br /> <br /> Speakers:<br /> Louis Archer, London Manager -- Tableau<br /> Marina Lindl, Sales Consultant -- Tableau <br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director -- Data Science Central